Bayesian growth curves using normal mixtures with nonparametric weights

L Scaccia, PJ Green

Research output: Contribution to journalArticle (Academic Journal)

15 Citations (Scopus)

Abstract

Reference growth curves estimate the distribution of a measurement as it changes according to some covariate, often age. We present a new methodology to estimate growth curves based on mixture models and splines. We model the distribution of the measurement with a mixture of normal distributions with an unknown number of components, and model dependence on the covariate through the weights, using smooth functions based on B-splines. In this way the growth curves respect the continuity of the covariate and there is no need for arbitrary grouping of the observations. The method is illustrated with data on triceps skinfold in Gambian girls and women.
Translated title of the contributionBayesian growth curves using normal mixtures with nonparametric weights
Original languageEnglish
Pages (from-to)308 - 331
JournalJournal of Computational and Graphical StatisticsCOMPUT GRAPH STAT
Volume12(2)
Publication statusPublished - Jun 2003

Bibliographical note

Publisher: Amer Statistical Assoc
Other identifier: IDS Number: 687HH

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